Video Bit-Rate Control Using Lagrange Multiplier Estimation
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Solution Overview
Problem
Existing video bit-rate control methods face challenges in achieving real-time encoding with controllable quality and bit-rate, particularly in balancing encoding speed and quality, especially in applications like digital video recorders and smartphones, where storage capacity and bandwidth are limited.
Innovation Solution
An image processing device and method that estimates a Lagrange multiplier and quantization parameter (QP) for each video frame based on designated distortion and maximum bit-rate values, using relation models to ensure stable and real-time quality control, allowing for encoding without pre-processing and maintaining quality during frame sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If constant bit-rate encoding is used, then encoding speed is maintained, but encoding quality becomes unstable especially during movement
Solution Approach 1:
The patent implements dynamic bit-rate allocation by estimating Lagrange multipliers and QP values for each frame based on motion characteristics and distortion metrics. The encoding parameters are adjusted frame-by-frame rather than fixed, allowing the system to adapt to content complexity and motion intensity, thus maintaining both encoding speed and quality stability
Solution Approach 2:
The system uses feedback mechanisms by calculating distortion values and comparing actual bit-rate consumption against target values. The Lagrange multiplier estimation incorporates feedback from previous frame encoding results, enabling continuous optimization of quality-stability while maintaining real-time encoding performance
2Device complexity
If fixed quantization parameter is used, then encoding process is simple, but quality control becomes poor
Solution Approach 1:
The patent dynamically changes QP parameters based on frame-specific distortion estimates and Lagrange multiplier calculations. Instead of using a fixed QP value, the system adjusts QP adaptively for each frame according to content complexity and motion characteristics, significantly improving quality control precision while adding manageable computational complexity
Solution Approach 2:
The system performs preliminary estimation of Lagrange multipliers and QP values before actual encoding. This pre-calculation step allows the encoder to prepare optimal parameters in advance, reducing runtime complexity while maintaining high quality control precision during the actual encoding process
3Manufacturing precision
If pre-processing is performed, then quality control improves, but real-time encoding capability is lost
Solution Approach 1:
The patent segments the video encoding process into independent frame-by-frame operations with localized parameter estimation. Each frame is processed independently using its own distortion metric and Lagrange multiplier calculation, eliminating the need for global pre-processing while maintaining quality control. This segmentation enables real-time encoding without sacrificing quality
Solution Approach 2:
The system performs self-service quality control by automatically estimating distortion values and calculating optimal QP parameters for each frame without external pre-processing intervention. The encoder adapts to content characteristics autonomously, maintaining both real-time capability and quality control precision
4Manufacturing precision
If higher bit-rate is used, then video quality improves, but storage capacity and bandwidth constraints are violated
Solution Approach 1:
The patent dynamically adjusts encoding parameters including QP and bit-rate allocation based on frame-specific distortion metrics and motion characteristics. By changing parameters adaptively rather than using fixed high bit-rate, the system achieves high video quality where needed while reducing bit-rate for simpler frames, thus respecting storage and bandwidth constraints
Solution Approach 2:
The system applies local quality optimization by allocating higher bit-rate to frames with high motion or complexity (where quality matters most) and lower bit-rate to simpler frames. This localized quality approach ensures high video quality for critical content while minimizing overall bit-rate consumption to fit storage and bandwidth limitations
Data Source
AI summary
The disclosure provides a method and an image processing device for video bit-rate control. The method includes the following steps. A designated distortion value and a maximum bit-rate value are received. A Lagrange multiplier and a quantization parameter (QP) of a current frame among a sequence of video frames are estimated according to the designated distortion value and the maximum bit-rate value. The current frame is encoded according to the estimated Lagrange multiplier and the estimated QP.